Overview

Dataset statistics

Number of variables19
Number of observations4761
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory349.4 B

Variable types

Categorical3
Text1
Unsupported1
Numeric14

Alerts

adult_bio_ratio is highly overall correlated with bio_risk_score and 1 other fieldsHigh correlation
adult_enrolment_ratio is highly overall correlated with age_18_greater and 1 other fieldsHigh correlation
age_0_5 is highly overall correlated with age_5_17 and 4 other fieldsHigh correlation
age_18_greater is highly overall correlated with adult_enrolment_ratio and 3 other fieldsHigh correlation
age_5_17 is highly overall correlated with age_0_5 and 6 other fieldsHigh correlation
bio_age_17_ is highly overall correlated with age_0_5 and 4 other fieldsHigh correlation
bio_age_5_17 is highly overall correlated with age_0_5 and 4 other fieldsHigh correlation
bio_risk_score is highly overall correlated with adult_bio_ratio and 1 other fieldsHigh correlation
child_bio_ratio is highly overall correlated with adult_bio_ratio and 1 other fieldsHigh correlation
child_enrolment_ratio is highly overall correlated with enrollment_risk_score and 1 other fieldsHigh correlation
enrollment_risk_score is highly overall correlated with adult_enrolment_ratio and 3 other fieldsHigh correlation
month_name_bio is highly overall correlated with month_name_enrolHigh correlation
month_name_enrol is highly overall correlated with month_name_bioHigh correlation
teen_enrolment_ratio is highly overall correlated with age_5_17 and 2 other fieldsHigh correlation
total_bio_updates is highly overall correlated with age_0_5 and 4 other fieldsHigh correlation
total_enrollments is highly overall correlated with age_0_5 and 5 other fieldsHigh correlation
month is an unsupported type, check if it needs cleaning or further analysisUnsupported
age_5_17 has 124 (2.6%) zerosZeros
age_18_greater has 1185 (24.9%) zerosZeros
teen_enrolment_ratio has 124 (2.6%) zerosZeros
adult_enrolment_ratio has 1185 (24.9%) zerosZeros

Reproduction

Analysis started2026-01-18 18:38:58.448230
Analysis finished2026-01-18 18:39:32.736117
Duration34.29 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

state
Categorical

Distinct36
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
Uttar Pradesh
520 
Madhya Pradesh
331 
Maharashtra
 
280
Assam
 
266
Bihar
 
265
Other values (31)
3099 

Length

Max length40
Median length16
Mean length9.920815
Min length3

Characters and Unicode

Total characters47233
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAndaman and Nicobar Islands
2nd rowAndaman and Nicobar Islands
3rd rowAndaman and Nicobar Islands
4th rowAndaman and Nicobar Islands
5th rowAndaman and Nicobar Islands

Common Values

ValueCountFrequency (%)
Uttar Pradesh520
 
10.9%
Madhya Pradesh331
 
7.0%
Maharashtra280
 
5.9%
Assam266
 
5.6%
Bihar265
 
5.6%
Gujarat257
 
5.4%
Karnataka218
 
4.6%
West Bengal213
 
4.5%
Rajasthan210
 
4.4%
Andhra Pradesh184
 
3.9%
Other values (26)2017
42.4%

Length

2026-01-18T18:39:32.960707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pradesh1190
17.7%
uttar520
 
7.7%
madhya331
 
4.9%
maharashtra280
 
4.2%
assam266
 
4.0%
bihar265
 
3.9%
gujarat257
 
3.8%
karnataka218
 
3.2%
west213
 
3.2%
bengal213
 
3.2%
Other values (37)2957
44.1%

Most occurring characters

ValueCountFrequency (%)
a10528
22.3%
h4290
 
9.1%
r4246
 
9.0%
s2905
 
6.2%
t2698
 
5.7%
d2627
 
5.6%
n2099
 
4.4%
e2069
 
4.4%
1949
 
4.1%
P1339
 
2.8%
Other values (33)12483
26.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)47233
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a10528
22.3%
h4290
 
9.1%
r4246
 
9.0%
s2905
 
6.2%
t2698
 
5.7%
d2627
 
5.6%
n2099
 
4.4%
e2069
 
4.4%
1949
 
4.1%
P1339
 
2.8%
Other values (33)12483
26.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)47233
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a10528
22.3%
h4290
 
9.1%
r4246
 
9.0%
s2905
 
6.2%
t2698
 
5.7%
d2627
 
5.6%
n2099
 
4.4%
e2069
 
4.4%
1949
 
4.1%
P1339
 
2.8%
Other values (33)12483
26.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)47233
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a10528
22.3%
h4290
 
9.1%
r4246
 
9.0%
s2905
 
6.2%
t2698
 
5.7%
d2627
 
5.6%
n2099
 
4.4%
e2069
 
4.4%
1949
 
4.1%
P1339
 
2.8%
Other values (33)12483
26.4%
Distinct943
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Memory size270.1 KiB
2026-01-18T18:39:33.447380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length31
Median length28
Mean length8.9287965
Min length3

Characters and Unicode

Total characters42510
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)0.4%

Sample

1st rowAndamans
2nd rowAndamans
3rd rowAndamans
4th rowAndamans
5th rowNicobar
ValueCountFrequency (%)
west131
 
2.2%
east93
 
1.6%
hills92
 
1.6%
nagar92
 
1.6%
south88
 
1.5%
north74
 
1.2%
delhi72
 
1.2%
62
 
1.0%
garo39
 
0.7%
khasi29
 
0.5%
Other values (914)5163
87.0%
2026-01-18T18:39:33.949704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a8076
19.0%
r3620
 
8.5%
i2663
 
6.3%
h2523
 
5.9%
n2197
 
5.2%
u2156
 
5.1%
l1580
 
3.7%
d1354
 
3.2%
t1342
 
3.2%
g1310
 
3.1%
Other values (52)15689
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)42510
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a8076
19.0%
r3620
 
8.5%
i2663
 
6.3%
h2523
 
5.9%
n2197
 
5.2%
u2156
 
5.1%
l1580
 
3.7%
d1354
 
3.2%
t1342
 
3.2%
g1310
 
3.1%
Other values (52)15689
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)42510
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a8076
19.0%
r3620
 
8.5%
i2663
 
6.3%
h2523
 
5.9%
n2197
 
5.2%
u2156
 
5.1%
l1580
 
3.7%
d1354
 
3.2%
t1342
 
3.2%
g1310
 
3.1%
Other values (52)15689
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)42510
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a8076
19.0%
r3620
 
8.5%
i2663
 
6.3%
h2523
 
5.9%
n2197
 
5.2%
u2156
 
5.1%
l1580
 
3.7%
d1354
 
3.2%
t1342
 
3.2%
g1310
 
3.1%
Other values (52)15689
36.9%

month
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size37.3 KiB

age_0_5
Real number (ℝ)

High correlation 

Distinct1812
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean733.49149
Minimum0
Maximum12270
Zeros37
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size37.3 KiB
2026-01-18T18:39:34.103533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q1134
median469
Q31018
95-th percentile2381
Maximum12270
Range12270
Interquartile range (IQR)884

Descriptive statistics

Standard deviation862.02678
Coefficient of variation (CV)1.1752376
Kurtosis15.226084
Mean733.49149
Median Absolute Deviation (MAD)390
Skewness2.7645113
Sum3492153
Variance743090.17
MonotonicityNot monotonic
2026-01-18T18:39:34.265295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
168
 
1.4%
239
 
0.8%
037
 
0.8%
336
 
0.8%
425
 
0.5%
622
 
0.5%
1221
 
0.4%
520
 
0.4%
1619
 
0.4%
1019
 
0.4%
Other values (1802)4455
93.6%
ValueCountFrequency (%)
037
0.8%
168
1.4%
239
0.8%
336
0.8%
425
 
0.5%
520
 
0.4%
622
 
0.5%
718
 
0.4%
816
 
0.3%
914
 
0.3%
ValueCountFrequency (%)
122701
< 0.1%
82551
< 0.1%
78441
< 0.1%
74861
< 0.1%
72911
< 0.1%
69811
< 0.1%
65701
< 0.1%
65301
< 0.1%
58911
< 0.1%
57741
< 0.1%

age_5_17
Real number (ℝ)

High correlation  Zeros 

Distinct1205
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean350.15375
Minimum0
Maximum6096
Zeros124
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size37.3 KiB
2026-01-18T18:39:34.404155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q132
median126
Q3400
95-th percentile1496
Maximum6096
Range6096
Interquartile range (IQR)368

Descriptive statistics

Standard deviation570.82464
Coefficient of variation (CV)1.6302114
Kurtosis14.746778
Mean350.15375
Median Absolute Deviation (MAD)113
Skewness3.28545
Sum1667082
Variance325840.77
MonotonicityNot monotonic
2026-01-18T18:39:34.540375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0124
 
2.6%
181
 
1.7%
260
 
1.3%
1349
 
1.0%
448
 
1.0%
645
 
0.9%
1242
 
0.9%
342
 
0.9%
1041
 
0.9%
1138
 
0.8%
Other values (1195)4191
88.0%
ValueCountFrequency (%)
0124
2.6%
181
1.7%
260
1.3%
342
 
0.9%
448
 
1.0%
537
 
0.8%
645
 
0.9%
728
 
0.6%
834
 
0.7%
925
 
0.5%
ValueCountFrequency (%)
60961
< 0.1%
48611
< 0.1%
47871
< 0.1%
47151
< 0.1%
46881
< 0.1%
45801
< 0.1%
45191
< 0.1%
43851
< 0.1%
43551
< 0.1%
43301
< 0.1%

age_18_greater
Real number (ℝ)

High correlation  Zeros 

Distinct312
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.386894
Minimum0
Maximum2404
Zeros1185
Zeros (%)24.9%
Negative0
Negative (%)0.0%
Memory size37.3 KiB
2026-01-18T18:39:34.680755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q327
95-th percentile144
Maximum2404
Range2404
Interquartile range (IQR)26

Descriptive statistics

Standard deviation99.729735
Coefficient of variation (CV)2.9870924
Kurtosis146.95054
Mean33.386894
Median Absolute Deviation (MAD)8
Skewness9.8263891
Sum158955
Variance9946.02
MonotonicityNot monotonic
2026-01-18T18:39:34.831576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01185
24.9%
1341
 
7.2%
2227
 
4.8%
3162
 
3.4%
10152
 
3.2%
4143
 
3.0%
11119
 
2.5%
5111
 
2.3%
786
 
1.8%
685
 
1.8%
Other values (302)2150
45.2%
ValueCountFrequency (%)
01185
24.9%
1341
 
7.2%
2227
 
4.8%
3162
 
3.4%
4143
 
3.0%
5111
 
2.3%
685
 
1.8%
786
 
1.8%
879
 
1.7%
969
 
1.4%
ValueCountFrequency (%)
24041
< 0.1%
18671
< 0.1%
16731
< 0.1%
15341
< 0.1%
11791
< 0.1%
11291
< 0.1%
11061
< 0.1%
10471
< 0.1%
10011
< 0.1%
9781
< 0.1%

total_enrollments
Real number (ℝ)

High correlation 

Distinct2249
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1117.0321
Minimum1
Maximum13877
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.3 KiB
2026-01-18T18:39:34.985226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q1216
median677
Q31555
95-th percentile3737
Maximum13877
Range13876
Interquartile range (IQR)1339

Descriptive statistics

Standard deviation1322.6638
Coefficient of variation (CV)1.1840875
Kurtosis8.6241746
Mean1117.0321
Median Absolute Deviation (MAD)547
Skewness2.3903503
Sum5318190
Variance1749439.6
MonotonicityNot monotonic
2026-01-18T18:39:35.141426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157
 
1.2%
233
 
0.7%
424
 
0.5%
323
 
0.5%
1617
 
0.4%
816
 
0.3%
1016
 
0.3%
1116
 
0.3%
515
 
0.3%
715
 
0.3%
Other values (2239)4529
95.1%
ValueCountFrequency (%)
157
1.2%
233
0.7%
323
0.5%
424
0.5%
515
 
0.3%
612
 
0.3%
715
 
0.3%
816
 
0.3%
99
 
0.2%
1016
 
0.3%
ValueCountFrequency (%)
138771
< 0.1%
106421
< 0.1%
105471
< 0.1%
98381
< 0.1%
94591
< 0.1%
92281
< 0.1%
91311
< 0.1%
91171
< 0.1%
89851
< 0.1%
88381
< 0.1%

child_enrolment_ratio
Real number (ℝ)

High correlation 

Distinct4125
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.68447549
Minimum0
Maximum1
Zeros37
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size37.3 KiB
2026-01-18T18:39:35.301359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.24
Q10.55405405
median0.73684211
Q30.85450517
95-th percentile0.95408895
Maximum1
Range1
Interquartile range (IQR)0.30045112

Descriptive statistics

Standard deviation0.21894749
Coefficient of variation (CV)0.31987631
Kurtosis0.23530108
Mean0.68447549
Median Absolute Deviation (MAD)0.13921535
Skewness-0.87654572
Sum3258.7878
Variance0.047938005
MonotonicityNot monotonic
2026-01-18T18:39:35.447190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1116
 
2.4%
037
 
0.8%
0.534
 
0.7%
0.666666666722
 
0.5%
0.857142857115
 
0.3%
0.87515
 
0.3%
0.333333333314
 
0.3%
0.833333333313
 
0.3%
0.7512
 
0.3%
0.611
 
0.2%
Other values (4115)4472
93.9%
ValueCountFrequency (%)
037
0.8%
0.0095238095241
 
< 0.1%
0.012820512821
 
< 0.1%
0.021739130431
 
< 0.1%
0.028571428571
 
< 0.1%
0.047619047621
 
< 0.1%
0.047872340431
 
< 0.1%
0.055555555561
 
< 0.1%
0.057692307691
 
< 0.1%
0.059255856681
 
< 0.1%
ValueCountFrequency (%)
1116
2.4%
0.99215686271
 
< 0.1%
0.99178644761
 
< 0.1%
0.99079189691
 
< 0.1%
0.99029126211
 
< 0.1%
0.98853868191
 
< 0.1%
0.98832684821
 
< 0.1%
0.98791540791
 
< 0.1%
0.98730964471
 
< 0.1%
0.98717948721
 
< 0.1%

teen_enrolment_ratio
Real number (ℝ)

High correlation  Zeros 

Distinct4099
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27465418
Minimum0
Maximum1
Zeros124
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size37.3 KiB
2026-01-18T18:39:35.591538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.038737446
Q10.12635379
median0.23177843
Q30.39130435
95-th percentile0.63873188
Maximum1
Range1
Interquartile range (IQR)0.26495056

Descriptive statistics

Standard deviation0.19211114
Coefficient of variation (CV)0.69946557
Kurtosis0.76419427
Mean0.27465418
Median Absolute Deviation (MAD)0.1225011
Skewness0.96801123
Sum1307.6286
Variance0.036906691
MonotonicityNot monotonic
2026-01-18T18:39:35.743554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0124
 
2.6%
0.533
 
0.7%
127
 
0.6%
0.333333333325
 
0.5%
0.2516
 
0.3%
0.12515
 
0.3%
0.142857142914
 
0.3%
0.166666666712
 
0.3%
0.412
 
0.3%
0.666666666711
 
0.2%
Other values (4089)4472
93.9%
ValueCountFrequency (%)
0124
2.6%
0.0078431372551
 
< 0.1%
0.0080385852091
 
< 0.1%
0.0082135523611
 
< 0.1%
0.0086206896551
 
< 0.1%
0.0092081031311
 
< 0.1%
0.0097087378641
 
< 0.1%
0.011461318051
 
< 0.1%
0.011673151751
 
< 0.1%
0.012084592151
 
< 0.1%
ValueCountFrequency (%)
127
0.6%
0.97826086961
 
< 0.1%
0.97142857142
 
< 0.1%
0.94444444441
 
< 0.1%
0.93103448281
 
< 0.1%
0.92948717951
 
< 0.1%
0.92307692311
 
< 0.1%
0.92
 
< 0.1%
0.89552238811
 
< 0.1%
0.88888888892
 
< 0.1%

adult_enrolment_ratio
Real number (ℝ)

High correlation  Zeros 

Distinct3236
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.040870329
Minimum0
Maximum1
Zeros1185
Zeros (%)24.9%
Negative0
Negative (%)0.0%
Memory size37.3 KiB
2026-01-18T18:39:35.896273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.00028176951
median0.0085763293
Q30.036322995
95-th percentile0.21237113
Maximum1
Range1
Interquartile range (IQR)0.036041226

Descriptive statistics

Standard deviation0.088875339
Coefficient of variation (CV)2.1745687
Kurtosis26.509255
Mean0.040870329
Median Absolute Deviation (MAD)0.0085763293
Skewness4.3948268
Sum194.58363
Variance0.0078988258
MonotonicityNot monotonic
2026-01-18T18:39:36.055501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01185
 
24.9%
0.33333333336
 
0.1%
0.028571428575
 
0.1%
0.255
 
0.1%
0.047619047625
 
0.1%
0.025641025645
 
0.1%
0.16666666675
 
0.1%
0.045
 
0.1%
0.02061855674
 
0.1%
0.034482758624
 
0.1%
Other values (3226)3532
74.2%
ValueCountFrequency (%)
01185
24.9%
0.00023551577961
 
< 0.1%
0.00024142926121
 
< 0.1%
0.00025497195311
 
< 0.1%
0.0002570033411
 
< 0.1%
0.00027495188341
 
< 0.1%
0.00028176951251
 
< 0.1%
0.00028384899231
 
< 0.1%
0.00030788177341
 
< 0.1%
0.00031545741321
 
< 0.1%
ValueCountFrequency (%)
13
0.1%
0.97337770381
 
< 0.1%
0.95238095241
 
< 0.1%
0.81
 
< 0.1%
0.77659574471
 
< 0.1%
0.7526881721
 
< 0.1%
0.6969696971
 
< 0.1%
0.68751
 
< 0.1%
0.681
 
< 0.1%
0.66666666671
 
< 0.1%

enrollment_risk_score
Real number (ℝ)

High correlation 

Distinct4323
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49716858
Minimum0.1
Maximum0.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.3 KiB
2026-01-18T18:39:36.218420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.34567308
Q10.45737705
median0.51473214
Q30.55430464
95-th percentile0.58544304
Maximum0.6
Range0.5
Interquartile range (IQR)0.096927587

Descriptive statistics

Standard deviation0.075935379
Coefficient of variation (CV)0.15273568
Kurtosis1.6725255
Mean0.49716858
Median Absolute Deviation (MAD)0.045160905
Skewness-1.2156149
Sum2367.0196
Variance0.0057661818
MonotonicityNot monotonic
2026-01-18T18:39:36.379367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6116
 
2.4%
0.328
 
0.6%
0.4525
 
0.5%
0.517
 
0.4%
0.557142857112
 
0.3%
0.411
 
0.2%
0.562510
 
0.2%
0.52510
 
0.2%
0.559
 
0.2%
0.489
 
0.2%
Other values (4313)4514
94.8%
ValueCountFrequency (%)
0.13
0.1%
0.10532445921
 
< 0.1%
0.10952380951
 
< 0.1%
0.15904255321
 
< 0.1%
0.19462365591
 
< 0.1%
0.21
 
< 0.1%
0.20337552741
 
< 0.1%
0.20961887481
 
< 0.1%
0.211
 
< 0.1%
0.21682900431
 
< 0.1%
ValueCountFrequency (%)
0.6116
2.4%
0.59764705881
 
< 0.1%
0.59753593431
 
< 0.1%
0.59723756911
 
< 0.1%
0.59708737861
 
< 0.1%
0.59656160461
 
< 0.1%
0.59649805451
 
< 0.1%
0.59637462241
 
< 0.1%
0.59619289341
 
< 0.1%
0.59615384621
 
< 0.1%

month_name_enrol
Categorical

High correlation 

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size261.3 KiB
September
944 
December
936 
November
934 
October
932 
July
296 
Other values (4)
719 

Length

Max length9
Median length8
Mean length7.1783239
Min length3

Characters and Unicode

Total characters34176
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSeptember
2nd rowOctober
3rd rowNovember
4th rowDecember
5th rowSeptember

Common Values

ValueCountFrequency (%)
September944
19.8%
December936
19.7%
November934
19.6%
October932
19.6%
July296
 
6.2%
April266
 
5.6%
June195
 
4.1%
May194
 
4.1%
March64
 
1.3%

Length

2026-01-18T18:39:36.514107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-18T18:39:36.647372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
september944
19.8%
december936
19.7%
november934
19.6%
october932
19.6%
july296
 
6.2%
april266
 
5.6%
june195
 
4.1%
may194
 
4.1%
march64
 
1.3%

Most occurring characters

ValueCountFrequency (%)
e8635
25.3%
r4076
11.9%
b3746
11.0%
m2814
 
8.2%
c1932
 
5.7%
t1876
 
5.5%
o1866
 
5.5%
p1210
 
3.5%
S944
 
2.8%
D936
 
2.7%
Other values (13)6141
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)34176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e8635
25.3%
r4076
11.9%
b3746
11.0%
m2814
 
8.2%
c1932
 
5.7%
t1876
 
5.5%
o1866
 
5.5%
p1210
 
3.5%
S944
 
2.8%
D936
 
2.7%
Other values (13)6141
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)34176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e8635
25.3%
r4076
11.9%
b3746
11.0%
m2814
 
8.2%
c1932
 
5.7%
t1876
 
5.5%
o1866
 
5.5%
p1210
 
3.5%
S944
 
2.8%
D936
 
2.7%
Other values (13)6141
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)34176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e8635
25.3%
r4076
11.9%
b3746
11.0%
m2814
 
8.2%
c1932
 
5.7%
t1876
 
5.5%
o1866
 
5.5%
p1210
 
3.5%
S944
 
2.8%
D936
 
2.7%
Other values (13)6141
18.0%

bio_age_5_17
Real number (ℝ)

High correlation 

Distinct3498
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4459.1657
Minimum0
Maximum51052
Zeros46
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size37.3 KiB
2026-01-18T18:39:36.807310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q1594
median2681
Q36341
95-th percentile14877
Maximum51052
Range51052
Interquartile range (IQR)5747

Descriptive statistics

Standard deviation5317.2404
Coefficient of variation (CV)1.1924294
Kurtosis7.7962616
Mean4459.1657
Median Absolute Deviation (MAD)2401
Skewness2.238711
Sum21230088
Variance28273045
MonotonicityNot monotonic
2026-01-18T18:39:36.993741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
046
 
1.0%
141
 
0.9%
327
 
0.6%
225
 
0.5%
622
 
0.5%
718
 
0.4%
1117
 
0.4%
415
 
0.3%
1215
 
0.3%
514
 
0.3%
Other values (3488)4521
95.0%
ValueCountFrequency (%)
046
1.0%
141
0.9%
225
0.5%
327
0.6%
415
 
0.3%
514
 
0.3%
622
0.5%
718
 
0.4%
89
 
0.2%
912
 
0.3%
ValueCountFrequency (%)
510521
< 0.1%
494371
< 0.1%
453291
< 0.1%
417811
< 0.1%
399041
< 0.1%
376701
< 0.1%
358161
< 0.1%
346401
< 0.1%
341841
< 0.1%
331791
< 0.1%

bio_age_17_
Real number (ℝ)

High correlation 

Distinct3485
Distinct (%)73.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4340.3367
Minimum0
Maximum59381
Zeros11
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size37.3 KiB
2026-01-18T18:39:37.153383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q1614
median2611
Q36068
95-th percentile14020
Maximum59381
Range59381
Interquartile range (IQR)5454

Descriptive statistics

Standard deviation5535.2361
Coefficient of variation (CV)1.2753011
Kurtosis15.307635
Mean4340.3367
Median Absolute Deviation (MAD)2296
Skewness3.0960893
Sum20664343
Variance30638838
MonotonicityNot monotonic
2026-01-18T18:39:37.298191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
226
 
0.5%
117
 
0.4%
316
 
0.3%
011
 
0.2%
410
 
0.2%
910
 
0.2%
2410
 
0.2%
409
 
0.2%
119
 
0.2%
69
 
0.2%
Other values (3475)4634
97.3%
ValueCountFrequency (%)
011
0.2%
117
0.4%
226
0.5%
316
0.3%
410
 
0.2%
59
 
0.2%
69
 
0.2%
77
 
0.1%
84
 
0.1%
910
 
0.2%
ValueCountFrequency (%)
593811
< 0.1%
519391
< 0.1%
516901
< 0.1%
502071
< 0.1%
486571
< 0.1%
471841
< 0.1%
442841
< 0.1%
432631
< 0.1%
425861
< 0.1%
420951
< 0.1%

total_bio_updates
Real number (ℝ)

High correlation 

Distinct3963
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8799.5024
Minimum1
Maximum89018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.3 KiB
2026-01-18T18:39:37.448634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile58
Q11355
median5639
Q312616
95-th percentile27555
Maximum89018
Range89017
Interquartile range (IQR)11261

Descriptive statistics

Standard deviation10113.759
Coefficient of variation (CV)1.1493558
Kurtosis8.2635618
Mean8799.5024
Median Absolute Deviation (MAD)4954
Skewness2.2637676
Sum41894431
Variance1.0228813 × 108
MonotonicityNot monotonic
2026-01-18T18:39:37.603582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
319
 
0.4%
217
 
0.4%
115
 
0.3%
411
 
0.2%
59
 
0.2%
67
 
0.1%
157
 
0.1%
76
 
0.1%
166
 
0.1%
86
 
0.1%
Other values (3953)4658
97.8%
ValueCountFrequency (%)
115
0.3%
217
0.4%
319
0.4%
411
0.2%
59
0.2%
67
 
0.1%
76
 
0.1%
86
 
0.1%
95
 
0.1%
104
 
0.1%
ValueCountFrequency (%)
890181
< 0.1%
839381
< 0.1%
830001
< 0.1%
826101
< 0.1%
822741
< 0.1%
800501
< 0.1%
754031
< 0.1%
746611
< 0.1%
712471
< 0.1%
692491
< 0.1%

child_bio_ratio
Real number (ℝ)

High correlation 

Distinct4580
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48280683
Minimum0
Maximum1
Zeros46
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size37.3 KiB
2026-01-18T18:39:38.363110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.14046823
Q10.37445887
median0.49140961
Q30.61591822
95-th percentile0.75999162
Maximum1
Range1
Interquartile range (IQR)0.24145934

Descriptive statistics

Standard deviation0.18429713
Coefficient of variation (CV)0.38172022
Kurtosis-0.094652876
Mean0.48280683
Median Absolute Deviation (MAD)0.12101984
Skewness-0.31594612
Sum2298.6433
Variance0.033965432
MonotonicityNot monotonic
2026-01-18T18:39:38.520139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
046
 
1.0%
0.333333333317
 
0.4%
0.2512
 
0.3%
111
 
0.2%
0.511
 
0.2%
0.210
 
0.2%
0.16666666675
 
0.1%
0.3755
 
0.1%
0.14285714295
 
0.1%
0.45
 
0.1%
Other values (4570)4634
97.3%
ValueCountFrequency (%)
046
1.0%
0.0030864197531
 
< 0.1%
0.0037243947861
 
< 0.1%
0.0066225165561
 
< 0.1%
0.0066445182721
 
< 0.1%
0.010869565221
 
< 0.1%
0.019417475731
 
< 0.1%
0.019480519481
 
< 0.1%
0.02061855671
 
< 0.1%
0.022471910111
 
< 0.1%
ValueCountFrequency (%)
111
0.2%
0.9629629631
 
< 0.1%
0.93333333331
 
< 0.1%
0.93220338981
 
< 0.1%
0.92839506171
 
< 0.1%
0.92346938781
 
< 0.1%
0.91666666671
 
< 0.1%
0.91277890471
 
< 0.1%
0.91038696541
 
< 0.1%
0.90693069311
 
< 0.1%

adult_bio_ratio
Real number (ℝ)

High correlation 

Distinct4580
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51719317
Minimum0
Maximum1
Zeros11
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size37.3 KiB
2026-01-18T18:39:38.681597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.24000838
Q10.38408178
median0.50859039
Q30.62554113
95-th percentile0.85953177
Maximum1
Range1
Interquartile range (IQR)0.24145934

Descriptive statistics

Standard deviation0.18429713
Coefficient of variation (CV)0.356341
Kurtosis-0.094652876
Mean0.51719317
Median Absolute Deviation (MAD)0.12101984
Skewness0.31594612
Sum2462.3567
Variance0.033965432
MonotonicityNot monotonic
2026-01-18T18:39:38.848341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
146
 
1.0%
0.666666666717
 
0.4%
0.7512
 
0.3%
011
 
0.2%
0.511
 
0.2%
0.810
 
0.2%
0.83333333335
 
0.1%
0.6255
 
0.1%
0.85714285715
 
0.1%
0.65
 
0.1%
Other values (4570)4634
97.3%
ValueCountFrequency (%)
011
0.2%
0.037037037041
 
< 0.1%
0.066666666671
 
< 0.1%
0.067796610171
 
< 0.1%
0.071604938271
 
< 0.1%
0.076530612241
 
< 0.1%
0.083333333331
 
< 0.1%
0.087221095331
 
< 0.1%
0.089613034621
 
< 0.1%
0.093069306931
 
< 0.1%
ValueCountFrequency (%)
146
1.0%
0.99691358021
 
< 0.1%
0.99627560521
 
< 0.1%
0.99337748341
 
< 0.1%
0.99335548171
 
< 0.1%
0.98913043481
 
< 0.1%
0.98058252431
 
< 0.1%
0.98051948051
 
< 0.1%
0.97938144331
 
< 0.1%
0.97752808991
 
< 0.1%

bio_risk_score
Real number (ℝ)

High correlation 

Distinct4580
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49312273
Minimum0.3
Maximum0.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.3 KiB
2026-01-18T18:39:39.000101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.35618729
Q10.44978355
median0.49656384
Q30.54636729
95-th percentile0.60399665
Maximum0.7
Range0.4
Interquartile range (IQR)0.096583738

Descriptive statistics

Standard deviation0.073718852
Coefficient of variation (CV)0.14949392
Kurtosis-0.094652876
Mean0.49312273
Median Absolute Deviation (MAD)0.048407935
Skewness-0.31594612
Sum2347.7573
Variance0.0054344691
MonotonicityNot monotonic
2026-01-18T18:39:39.185754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.346
 
1.0%
0.433333333317
 
0.4%
0.412
 
0.3%
0.711
 
0.2%
0.511
 
0.2%
0.3810
 
0.2%
0.36666666675
 
0.1%
0.455
 
0.1%
0.35714285715
 
0.1%
0.465
 
0.1%
Other values (4570)4634
97.3%
ValueCountFrequency (%)
0.346
1.0%
0.30123456791
 
< 0.1%
0.30148975791
 
< 0.1%
0.30264900661
 
< 0.1%
0.30265780731
 
< 0.1%
0.30434782611
 
< 0.1%
0.30776699031
 
< 0.1%
0.30779220781
 
< 0.1%
0.30824742271
 
< 0.1%
0.3089887641
 
< 0.1%
ValueCountFrequency (%)
0.711
0.2%
0.68518518521
 
< 0.1%
0.67333333331
 
< 0.1%
0.67288135591
 
< 0.1%
0.67135802471
 
< 0.1%
0.66938775511
 
< 0.1%
0.66666666671
 
< 0.1%
0.66511156191
 
< 0.1%
0.66415478621
 
< 0.1%
0.66277227721
 
< 0.1%

month_name_bio
Categorical

High correlation 

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size261.3 KiB
September
944 
December
936 
November
934 
October
932 
July
296 
Other values (4)
719 

Length

Max length9
Median length8
Mean length7.1783239
Min length3

Characters and Unicode

Total characters34176
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSeptember
2nd rowOctober
3rd rowNovember
4th rowDecember
5th rowSeptember

Common Values

ValueCountFrequency (%)
September944
19.8%
December936
19.7%
November934
19.6%
October932
19.6%
July296
 
6.2%
April266
 
5.6%
June195
 
4.1%
May194
 
4.1%
March64
 
1.3%

Length

2026-01-18T18:39:39.324067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-18T18:39:39.429782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
september944
19.8%
december936
19.7%
november934
19.6%
october932
19.6%
july296
 
6.2%
april266
 
5.6%
june195
 
4.1%
may194
 
4.1%
march64
 
1.3%

Most occurring characters

ValueCountFrequency (%)
e8635
25.3%
r4076
11.9%
b3746
11.0%
m2814
 
8.2%
c1932
 
5.7%
t1876
 
5.5%
o1866
 
5.5%
p1210
 
3.5%
S944
 
2.8%
D936
 
2.7%
Other values (13)6141
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)34176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e8635
25.3%
r4076
11.9%
b3746
11.0%
m2814
 
8.2%
c1932
 
5.7%
t1876
 
5.5%
o1866
 
5.5%
p1210
 
3.5%
S944
 
2.8%
D936
 
2.7%
Other values (13)6141
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)34176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e8635
25.3%
r4076
11.9%
b3746
11.0%
m2814
 
8.2%
c1932
 
5.7%
t1876
 
5.5%
o1866
 
5.5%
p1210
 
3.5%
S944
 
2.8%
D936
 
2.7%
Other values (13)6141
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)34176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e8635
25.3%
r4076
11.9%
b3746
11.0%
m2814
 
8.2%
c1932
 
5.7%
t1876
 
5.5%
o1866
 
5.5%
p1210
 
3.5%
S944
 
2.8%
D936
 
2.7%
Other values (13)6141
18.0%

Interactions

2026-01-18T18:39:29.439661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:38:59.841100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:01.643778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:03.745127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:05.962329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:08.247636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:10.413825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:12.483511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:14.814178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:16.721302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:19.758225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:21.913527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:24.105605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:26.637248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:29.627254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:38:59.981330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:01.761158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:03.866449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:06.131240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:08.371186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:10.561033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:12.655796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:14.958461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:17.261067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:19.962636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:22.035725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:24.316104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:26.791525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:29.775539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:00.106422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:01.872010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:03.993163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:06.323802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:08.505496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:10.718143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:12.815677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:15.111968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:17.459188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:20.218342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:22.179832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:24.481284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:26.953242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:29.918809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:00.234458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:02.000526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:04.111192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:06.533127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:08.634245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:10.843356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:12.996399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:15.270671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:17.642743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:20.378891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:22.299286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:24.651628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:27.114707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:30.056947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:00.377076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:02.158405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:04.260483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:06.725552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:08.780144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:10.986323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:13.188257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:15.432650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:17.851771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:20.509737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:22.432758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:24.825748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:27.300942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:30.204534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:00.499396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:02.282058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:04.392477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:06.920602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:08.917405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:11.151052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:13.343350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:15.562553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:18.071526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:20.627023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:22.563583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:24.990168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:28.152823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:30.395484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:00.623108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:02.404142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:04.572969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:07.110763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:09.037571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:11.287645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:13.495538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:15.714721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:18.257260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:20.767324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:22.696885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:25.189365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:28.314849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:30.615391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:00.740175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:02.518499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:04.751609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:07.284542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:09.174876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:11.432686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:13.698591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:15.841475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:18.423941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:20.903783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:22.840817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:25.402663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:28.487709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:30.811475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:00.860940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:02.646821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:04.920278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:07.420925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:09.306225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:11.570377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:13.891722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:15.972776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:18.598569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:21.033093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:22.997491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:25.573521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:28.609630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:31.007779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:00.986048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:02.774480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:05.089225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:07.554137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:09.772856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:11.696521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:14.049441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:16.091666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-18T18:39:21.192986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-18T18:39:23.893281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:26.490341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T18:39:29.277031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-18T18:39:39.564815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
adult_bio_ratioadult_enrolment_ratioage_0_5age_18_greaterage_5_17bio_age_17_bio_age_5_17bio_risk_scorechild_bio_ratiochild_enrolment_ratioenrollment_risk_scoremonth_name_biomonth_name_enrolstateteen_enrolment_ratiototal_bio_updatestotal_enrollments
adult_bio_ratio1.000-0.164-0.157-0.184-0.1890.029-0.353-1.000-1.0000.1110.1130.0810.0810.245-0.104-0.169-0.176
adult_enrolment_ratio-0.1641.0000.0520.8620.2680.2110.2450.1640.164-0.456-0.5280.1590.1590.2280.2580.2310.157
age_0_5-0.1570.0521.0000.4260.7910.7290.7310.1570.1570.0950.1090.0900.0900.122-0.0190.7440.963
age_18_greater-0.1840.8620.4261.0000.6120.4500.4850.1840.184-0.456-0.5060.0720.0720.1700.3220.4770.543
age_5_17-0.1890.2680.7910.6121.0000.6280.6590.1890.189-0.455-0.4300.0790.0790.1890.5220.6560.908
bio_age_17_0.0290.2110.7290.4500.6281.0000.897-0.029-0.029-0.009-0.0100.1160.1160.1950.0430.9700.717
bio_age_5_17-0.3530.2450.7310.4850.6590.8971.0000.3530.353-0.062-0.0620.1190.1190.1680.0980.9750.731
bio_risk_score-1.0000.1640.1570.1840.189-0.0290.3531.0001.000-0.111-0.1130.0810.0810.2460.1040.1690.176
child_bio_ratio-1.0000.1640.1570.1840.189-0.0290.3531.0001.000-0.111-0.1130.0810.0810.2450.1040.1690.176
child_enrolment_ratio0.111-0.4560.095-0.456-0.455-0.009-0.062-0.111-0.1111.0000.9940.1690.1690.363-0.945-0.038-0.117
enrollment_risk_score0.113-0.5280.109-0.506-0.430-0.010-0.062-0.113-0.1130.9941.0000.1890.1890.347-0.910-0.038-0.101
month_name_bio0.0810.1590.0900.0720.0790.1160.1190.0810.0810.1690.1891.0001.0000.1020.1280.1260.089
month_name_enrol0.0810.1590.0900.0720.0790.1160.1190.0810.0810.1690.1891.0001.0000.1020.1280.1260.089
state0.2450.2280.1220.1700.1890.1950.1680.2460.2450.3630.3470.1020.1021.0000.3440.2020.150
teen_enrolment_ratio-0.1040.258-0.0190.3220.5220.0430.0980.1040.104-0.945-0.9100.1280.1280.3441.0000.0740.179
total_bio_updates-0.1690.2310.7440.4770.6560.9700.9750.1690.169-0.038-0.0380.1260.1260.2020.0741.0000.738
total_enrollments-0.1760.1570.9630.5430.9080.7170.7310.1760.176-0.117-0.1010.0890.0890.1500.1790.7381.000

Missing values

2026-01-18T18:39:32.156523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-18T18:39:32.494604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

statedistrictmonthage_0_5age_5_17age_18_greatertotal_enrollmentschild_enrolment_ratioteen_enrolment_ratioadult_enrolment_ratioenrollment_risk_scoremonth_name_enrolbio_age_5_17bio_age_17_total_bio_updateschild_bio_ratioadult_bio_ratiobio_risk_scoremonth_name_bio
0Andaman and Nicobar IslandsAndamans2025-092340270.8518520.1481480.00.555556September762413170.2397480.7602520.395899September
1Andaman and Nicobar IslandsAndamans2025-101500151.0000000.0000000.00.600000October431391820.2362640.7637360.394505October
2Andaman and Nicobar IslandsAndamans2025-111300131.0000000.0000000.00.600000November481742220.2162160.7837840.386486November
3Andaman and Nicobar IslandsAndamans2025-121910200.9500000.0500000.00.585000December902323220.2795030.7204970.411801December
4Andaman and Nicobar IslandsNicobar2025-094160470.8723400.1276600.00.561702September931552480.3750000.6250000.450000September
5Andaman and Nicobar IslandsNicobar2025-10640100.6000000.4000000.00.480000October3663990.3636360.6363640.445455October
6Andaman and Nicobar IslandsNicobar2025-111200121.0000000.0000000.00.600000November132581900.6947370.3052630.577895November
7Andaman and Nicobar IslandsNicobar2025-1251060.8333330.1666670.00.550000December212532650.8000000.2000000.620000December
8Andaman and Nicobar IslandsNicobars2025-0910011.0000000.0000000.00.600000September1011.0000000.0000000.700000September
9Andaman and Nicobar IslandsNorth And Middle Andaman2025-093820400.9500000.0500000.00.585000September5591877460.7493300.2506700.599732September
statedistrictmonthage_0_5age_5_17age_18_greatertotal_enrollmentschild_enrolment_ratioteen_enrolment_ratioadult_enrolment_ratioenrollment_risk_scoremonth_name_enrolbio_age_5_17bio_age_17_total_bio_updateschild_bio_ratioadult_bio_ratiobio_risk_scoremonth_name_bio
4751West BengalWest Midnapore2025-0962622118480.7382080.2606130.0011790.521226September622184324650.2523330.7476670.400933September
4752West BengalWest Midnapore2025-1039112505160.7577520.2422480.0000000.527326October283152418070.1566130.8433870.362645October
4753West BengalWest Midnapore2025-1142312815520.7663040.2318840.0018120.529529November447240528520.1567320.8432680.362693November
4754West BengalWest Midnapore2025-122076612740.7554740.2408760.0036500.525912December435173221670.2007380.7992620.380295December
4755West Bengalhooghly2025-0912030.3333330.6666670.0000000.400000September4480.5000000.5000000.500000September
4756West Bengalhooghly2025-1010011.0000000.0000000.0000000.600000October1450.2000000.8000000.380000October
4757West Bengalhooghly2025-1130031.0000000.0000000.0000000.600000November015150.0000001.0000000.300000November
4758West Bengalhooghly2025-1230031.0000000.0000000.0000000.600000December39120.2500000.7500000.400000December
4759West Bengalnadia2025-0910011.0000000.0000000.0000000.600000September0110.0000001.0000000.300000September
4760West Bengalnadia2025-1011020.5000000.5000000.0000000.450000October0330.0000001.0000000.300000October